The Enhanced Jump-Wing SVI Parameterization
Implied volatility surfaces are a cornerstone of options modeling, and their accuracy is critical in both pricing and risk calibration. Talos improves upon a well-known parameterization to simplify the handling of these surfaces while preserving precision and invertibility.
The Enhanced Jump-Wing SVI Parameterization
Introduction
Implied volatility surfaces are a cornerstone of options modeling, and their accuracy is critical in both pricing and risk calibration. Talos improves upon a well-known parameterization to simplify the handling of these surfaces while preserving precision and invertibility.
Abstract: Given the standard raw SVI (stochastic implied volatility) parameterization, we look at an equivalent parameterization that is very similar to the well-known Jump-Wing SVI parameterization. We call this new parameterization the Enhanced Jump-Wing SVI parameterization and show that it can be inverted to recover the raw SVI parameters for a wider range of parameters compared to the traditional Jump-Wing SVI parameterization. We explicitly derive the formula to recover the raw SVI parameters from the Enhanced Jump-Wing SVI parameters and then provide a vectorized implementation of the formula.
Download the full PDF to learn how Talos’s Enhanced Jump-Wing SVI model can improve your volatility surface calibration, streamline interpolation and extrapolation, and support robust risk modeling across option maturities.
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